Recent developments in robotic and sensor hardware make data collection with mobile robots (ground or aerial) feasible and affordable to a wide population of users. The newly emergent applications, such as precision agriculture, weather damage assessment, or personal home security often do not satisfy the simplifying assumptions made by previous research: the explored areas have complex shapes and obstacles, multiple phenomena need to be sensed and estimated simultaneously and the measured quantities might change during observations. The future progress of path planning and estimation algorithms requires a new generation of benchmarks that provide representative environments and scoring methods that capture the demands of these applications. This paper describes the Waterberry Farms benchmark (WBF) that models a precision agriculture application at a Florida farm growing multiple crop types. The benchmark captures the dynamic nature of the spread of plant diseases and variations of soil humidity while the scoring system measures the performance of a given combination of a movement policy and an information model estimator. By benchmarking several examples of representative path planning and estimator algorithms, we demonstrate WBF's ability to provide insight into their properties and quantify future progress.

翻译：近年来，机器人与传感器硬件的发展使移动机器人（地面或空中）的数据采集在广泛用户群体中变得可行且经济。精密农业、天气灾害评估或个人家庭安防等新兴应用通常无法满足先前研究做出的简化假设：探索区域具有复杂形状和障碍物，需同时感知和估计多种现象，且观测期间被测量值可能发生变化。路径规划与估计算法的未来进展需要新一代基准测试，提供能体现这些应用需求的代表性环境和评分方法。本文描述的Waterberry Farms基准测试（WBF）以佛罗里达州某种植多种作物的农场为模型，模拟精密农业应用场景。该基准测试捕捉了植物病害传播和土壤湿度变化的动态特性，其评分系统用于衡量特定移动策略与信息模型估计器组合的性能。通过对若干代表性路径规划与估计算法进行基准测试，我们验证了WBF在揭示算法特性及量化未来进展方面的能力。